System for Extracting Header Labels for Header Cells in Tables Having Complex Header Structures

ABSTRACT

A method, system and computer-usable medium are disclosed for associating data cells with headers and header labels. In certain embodiments, a table having rows and columns is received, wherein the table includes a plurality of cells, wherein each cell is populated with at least one of a header name, data value, or no information. A determination is made as to whether a cell is a header cell or data cell. If the cell is a header cell, current list of column and current list of row headers are dynamically updated. The current list of column and row headers are assigned to the cell regardless of whether the cell is a header cell or data cell. Headers associated with header cells are used to identify label candidates for the header name of the header cell. The labels may be used to provide additional context for headers within a data cell.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method, system andcomputer-usable medium for extracting header labels for header cells intables having complex header structures to provide enhanced context fortable data.

Description of the Related Art

With the increased usage of computing networks, such as the Internet,humans are currently inundated and overwhelmed with the amount ofinformation available to them from various structured and unstructuredsources. However, information gaps abound as users try to piece togetherwhat they can find that they believe to be relevant during searches forinformation on various subjects. To assist with such searches, recentresearch has been directed to generating knowledge management systemswhich may take an input, analyze it, and return results indicative ofthe most probable results to the input. Knowledge management systemsprovide automated mechanisms for searching through a knowledge base withnumerous sources of content, e.g., electronic documents, and analyzethem with regard to an input to determine a result and a confidencemeasure as to how accurate the result is in relation to the input.

One such knowledge management system is the IBM Watson™ system availablefrom International Business Machines (IBM) Corporation of Armonk, N.Y.The IBM Watson™ system is an application of advanced natural languageprocessing, information retrieval, knowledge representation andreasoning, and machine learning technologies to the field of open domainquestion answering. The IBM Watson™ system is built on IBM's DeepQAtechnology used for hypothesis generation, massive evidence gathering,analysis, and scoring. DeepQA takes an input question, analyzes it,decomposes the question into constituent parts, generates one or morehypothesis based on the decomposed question and results of a primarysearch of answer sources, performs hypothesis and evidence scoring basedon a retrieval of evidence from evidence sources, performs synthesis ofthe one or more hypothesis, and based on trained models, performs afinal merging and ranking to output an answer to the input questionalong with a confidence measure.

SUMMARY OF THE INVENTION

A method, system and computer-usable medium are disclosed forassociating data cells with headers and header labels. In certainembodiments, the method comprises: receiving a table having rows andcolumns, wherein the table includes a plurality of cells, wherein eachcell is populated with at least one of a header name, data value, or noinformation; determining whether a cell is a header cell or data cell;if the cell is a header cell, dynamically updating a current list ofcolumn headers; dynamically updating a current list of row headers;dynamically updating a list of header names for the header cell usingthe current list of column headers and current list of row headers; uponencountering a data cell, assigning the current list of column and rowheaders to the data cell; and identifying label candidates for theheader cell from the list of header names; and assigning one or morelabel candidates as labels to the header cell.

Certain embodiments relate to a system comprising: a processor; a databus coupled to the processor; and a non-transitory, computer-readablestorage medium embodying computer program code, the non-transitory,computer-readable storage medium being coupled to the data bus, thecomputer program code interacting with a plurality of computeroperations and comprising instructions executable by the processor andconfigured for: receiving a table having rows and columns, wherein thetable includes a plurality of cells, wherein each cell is populated withat least one of a header name, data value, or no information;determining whether a cell is a header cell or data cell; if the cell isa header cell, dynamically updating a current list of column headers;dynamically updating a current list of row headers; dynamically updatinga list of header names for the header cell using the current list ofcolumn headers and current list of row headers; upon encountering a datacell, assigning the current list of column and row headers to the datacell; and identifying label candidates for the header cell from the listof header names; and assigning one or more label candidates as labels tothe header cell

Certain embodiments relate to a non-transitory, computer-readablestorage medium embodying computer program code, the computer programcode comprising computer executable instructions configured for:receiving a table having rows and columns, wherein the table includes aplurality of cells, wherein each cell is populated with at least one ofa header name, data value, or no information; determining whether a cellis a header cell or data cell; if the cell is a header cell, dynamicallyupdating a current list of column headers; and dynamically updating alist of header names for the header cell using the current list ofcolumn headers and current list of row headers; upon encountering a datacell, assigning the current list of column and row headers to the datacell; and identifying label candidates for the header cell from the listof header names; and assigning one or more label candidates as labels tothe header cell

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 shows a schematic diagram of one illustrative embodiment of aquestion/answer (QA) system.

FIG. 2 shows a simplified block diagram of an information processingsystem capable of performing computing operations.

FIG. 3 depicts one example of tabular information that may be used toprovide an understanding of certain embodiments of a header labelassignment system.

FIG. 4 is a flowchart depicting exemplary operations that may beexecuted in certain embodiments of the header label assignment system.

FIG. 5 is another flowchart depicting exemplary operations that may beexecuted in certain embodiments of the header label assignment system.

FIG. 6 is a flowchart depicting exemplary operations that may beexecuted when a determination is made that the cell at the indexed rowand indexed column is a data cell.

FIG. 7 is a flowchart depicting exemplary operations that may beexecuted to identify label candidates using header cell objects.

FIG. 8 shows one example of a data structure that may be generated fromthe table information shown in FIG. 3 using certain embodiments of theheader label assignment system.

FIG. 9 shows another example of a table 900 including information fromwhich header labels may be extracted to enhance the context of the datacells.

FIG. 10 depicts one example of pseudocode that may be used as a basis toimplement certain embodiments of the header label assignment system.

DETAILED DESCRIPTION

The present invention may be a system, a method, and/or a computerprogram product. In addition, selected aspects of the present inventionmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, microcode,etc.), or an embodiment combining software and/or hardware aspects thatmay all generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects of the present invention may take theform of computer program product embodied in a computer-readable storagemedium, or media, having computer-readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer-readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer-readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer-readable storage medium includes the following: a portablecomputer diskette, a hard disk, a dynamic or static random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), a magnetic storage device, a portableCompact Disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer-readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer-readable program instructions described herein can bedownloaded to respective computing/processing devices from acomputer-readable storage medium or to an external computer or externalstorage device via a network, for example, the Internet, a PublicSwitched Circuit Network (PSTN), a packet-based network, a personal areanetwork (PAN), a local area network (LAN), a wide area network (WAN), awireless network, or any suitable combination thereof. The network maycomprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer-readable programinstructions from the network and forwards the computer-readable programinstructions for storage in a computer-readable storage medium withinthe respective computing/processing device.

Computer-readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine-dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language, Hypertext Precursor (PHP), or similar programminglanguages. The computer-readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer, or entirely on the remote computer or server orcluster of servers. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga LAN or a WAN, or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer-readableprogram instructions by utilizing state information of thecomputer-readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer-readable program instructions.

These computer-readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer-readable program instructionsmay also be stored in a computer-readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that thecomputer-readable storage medium having instructions stored thereincomprises an article of manufacture including instructions whichimplement aspects of the function/act specified in the flowchart and/orblock diagram block or blocks.

The computer-readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce acomputer-implemented process, such that the instructions which executeon the computer, other programmable apparatus, or other device implementthe functions/acts specified in the flowchart and/or block diagram blockor blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a sub-system, module, segment,or portion of instructions, which comprises one or more executableinstructions for implementing the specified logical function(s). In somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 shows a schematic diagram of one illustrative embodiment of a QAsystem 100 and a question prioritization system 110 connected to acomputer network 140 to operate as a conversational system. The QAsystem 100 includes a knowledge manager 104 that is connected to aknowledge base 106 and configured to provide QA generation functionalityfor one or more content creators and/or users 130 who submit contentacross the network 140 to the QA system 100. To assist with efficientsorting and presentation of questions to the QA system 100, the questionprioritization system 110 may be connected to the computer network 140to receive user questions, and may include a plurality of sub-systemswhich interact with cognitive systems, like the QA system 100, toprioritize questions or requests being submitted to the QA system 100.

The Named Entity sub-system 112 receives and processes each question 111by using natural language processing (NLP) to analyze each question andextract question topic information contained in the question, such asnamed entities, phrases, urgent terms, and/or other specified termswhich are stored in one or more domain entity dictionaries 113. Byleveraging a plurality of pluggable domain dictionaries 113 relating todifferent domains or areas (e.g., travel, healthcare, electronics, gameshows, financial services, etc.), the domain dictionary 113 enablescritical and urgent words (e.g., “threat level”) from different domains(e.g., “travel”) to be identified in each question based on theirpresence in the domain dictionary 113. To this end, the Named Entitysub-system 112 may use an NLP routine to identify the question topicinformation in each question. As used herein, “NLP” broadly refers tothe field of computer science, artificial intelligence, and linguisticsconcerned with the interactions between computers and human (natural)languages. In this context, NLP is related to the area of human-computerinteraction and Natural Language understanding by computer systems thatenable computer systems to derive meaning from human or Natural Languageinput. For example, NLP can be used to derive meaning from ahuman-oriented question such as, “What is tallest mountain in NorthAmerica?” and to identify specified terms, such as named entities,phrases, or urgent terms contained in the question. The processidentifies key terms and attributes in the question and compares theidentified terms to the stored terms in the domain dictionary 113.

The Question Priority Manager sub-system 114 performs additionalprocessing on each question to extract question context information115A. In addition, or in the alternative, the Question Priority Managersub-system 114 may also extract server performance information 115B forthe question prioritization system 110 and/or QA system 100. In selectedembodiments, the extracted question context information 115A may includedata that identifies the user context and location when the question wassubmitted or received. For example, the extracted question contextinformation 115A may include data that identifies the user who submittedthe question (e.g., through login credentials), the device or computerwhich sent the question, the channel over which the question wassubmitted, or any combination thereof. Other examples may include thelocation of the user or device that sent the question, any specialinterest location indicator (e.g., hospital, public-safety answeringpoint, etc.), other context-related data for the question, or anycombination thereof. In certain embodiments, the location information isdetermined through the use of a Geographical Positioning System (GPS)satellite 168. In these embodiments, a handheld computer or mobiletelephone 150, or other device, uses signals transmitted by the GPSsatellite 168 to generate location information, which in turn isprovided via the computer network 140 to the Question Priority Managersub-system 114 for processing.

In various embodiments, the source for the extracted context information115A may be a data source 166 accessed through the computer network 140.Examples of a data source 166 include systems that provide telemetryinformation, such as medical information collected from medicalequipment used to monitor a patient's health, environment informationcollected from a facilities management system, or traffic flowinformation collected from a transportation monitoring system. Incertain embodiments, the data source 166 may be a storage area network(SAN) or other network-based repositories of data.

In various embodiments, the data source 166 may provide data directly orindirectly collected from “big data” sources. In general, big datarefers to a collection of datasets so large and complex that traditionaldatabase management tools and data processing approaches are inadequate.These datasets can originate from a wide variety of sources, includingcomputer systems (e.g., 156, 158, 162), mobile devices (e.g., 150, 152,154), financial transactions, streaming media, social media, as well assystems (e.g., 166) commonly associated with a wide variety offacilities and infrastructure (e.g., buildings, factories,transportation systems, power grids, pipelines, etc.). Big data, whichis typically a combination of structured, unstructured, andsemi-structured data poses multiple challenges, including its capture,curation, storage, transfer, search, querying, sharing, analysis andvisualization.

The Question Priority Manager sub-system 114 may also determine orextract selected server performance data 115B for the processing of eachquestion. In certain embodiments, the server performance information115B may include operational metric data relating to the availableprocessing resources at the question prioritization system 110 and/or QAsystem 100, such as operational or run-time data, CPU utilization data,available disk space data, bandwidth utilization data, and so forth. Aspart of the extracted information 115A/B, the Question Priority Managersub-system 114 may identify the Service Level Agreement (SLA) or Qualityof Service (QoS) processing requirements that apply to the questionbeing analyzed, the history of analysis and feedback for the question orsubmitting user, and the like. Using the question topic information andextracted question context 115A. and/or server performance information115B, the Question Priority Manager sub-system 114 is configured topopulate feature values for the Priority Assignment Model 116. Invarious embodiments, the Priority Assignment Model 116 provides amachine learning predictive model for generating target priority valuesfor the question, such as by using an artificial intelligence (AI)approaches known to those of skill in the art. In certain embodiments,the AI logic is used to determine and assign a question urgency value toeach question for purposes of prioritizing the response processing ofeach question by the QA system 100.

The Prioritization Manager sub-system 117 performs additional sort orrank processing to organize the received questions based on at least theassociated target priority values such that high priority questions areput to the front of a prioritized question queue 118 for output asprioritized questions 119. In the question queue 118 of thePrioritization Manager sub-system 117, the highest priority question isplaced at the front of the queue for delivery to the assigned QA system100. In selected embodiments, the prioritized questions 119 from thePrioritization Manager sub-system 117 that have a specified targetpriority value may be assigned to a particular pipeline (e.g., QA systempipeline 100A, 100B) in the QA system 100. As will be appreciated, thePrioritization Manager sub-system 117 may use the question queue 118 asa message queue to provide an asynchronous communications protocol fordelivering prioritized questions 119 to the QA system 100. Consequently,the Prioritization Manager sub-system 117 and QA system 100 do not needto interact with a question queue 118 at the same time by storingprioritized questions in the question queue 118 until the QA system 100retrieves them. In this way, a wider asynchronous network supports thepassing of prioritized questions 119 as messages between different QAsystem pipelines 100A, 100B, connecting multiple applications andmultiple operating systems. Messages can also be passed from queue toqueue in order for a message to reach the ultimate desired recipient. Anexample of a commercial implementation of such messaging software isIBM's WebSphere MQ (previously MQ Series). In selected embodiments, theorganizational function of the Prioritization Manager sub-system 117 maybe configured to convert over-subscribing questions into asynchronousresponses, even if they were asked in a synchronized fashion.

The QA system 100 may include one or more QA system pipelines 100A,100B, each of which includes a computing device 104 comprising one ormore processors and one or more memories. The QA system pipelines 100A,100B may likewise include potentially any other computing deviceelements generally known in the art including buses, storage devices,communication interfaces, and the like. In various embodiments, thesecomputing device elements may be implemented to process questionsreceived over the network 140 from one or more content creator and/orusers 130 at computing devices (e.g., 150, 152, 154, 156, 158, 162). Incertain embodiments, the one or more content creator and/or users 130are connected over the network 140 for communication with each other andwith other devices or components via one or more wired and/or wirelessdata communication links, where each communication link may comprise oneor more of wires, routers, switches, transmitters, receivers, or thelike. In this networked arrangement, the QA system 100 and network 140may enable QA generation functionality for one or more content users130. Other embodiments of QA system 100 may be used with components,systems, sub-systems, and/or devices other than those that are depictedherein.

In each QA system pipeline 100A, 100B, a prioritized question 119 isreceived and prioritized for processing to generate an answer 120. Insequence, prioritized questions 119 are de-queued from the sharedquestion queue 118, from which they are de-queued by the pipelineinstances for processing in priority order rather than insertion order.In selected embodiments, the question queue 118 may be implemented basedon a “priority heap” data structure. During processing within a QAsystem pipeline (e.g., 100A, 100B), questions may be split into multiplesubtasks, which run concurrently. In various embodiments, a singlepipeline instance may process a number of questions concurrently, butonly a certain number of subtasks. In addition, each QA system pipeline100A, 100B may include a prioritized queue (not shown) to manage theprocessing order of these subtasks, with the top-level prioritycorresponding to the time that the corresponding question started (i.e.,earliest has highest priority). However, it will be appreciated thatsuch internal prioritization within each QA system pipeline 100A, 100Bmay be augmented by the external target priority values generated foreach question by the Question Priority Manager sub-system 114 to takeprecedence, or ranking priority, over the question start time. In thisway, more important or higher priority questions can “fast track”through a QA system pipeline 100A, 100B if it is busy withalready-running questions.

In the QA system 100, the knowledge manager 104 may be configured toreceive inputs from various sources. For example, knowledge manager 104may receive input from the question prioritization system 110, network140, a knowledge base or corpus of electronic documents 107 or otherdata, semantic data 108, content creators, and/or users 130, and otherpossible sources of input. In selected embodiments, some or all of theinputs to knowledge manager 104 may be routed through the network 140and/or the question prioritization system 110. The various computingdevices (e.g., 150, 152, 154, 156, 158, 162) on the network 140 mayinclude access points for content creators and/or users 130. Some of thecomputing devices may include devices for a database storing a corpus ofdata as the body of information used by the knowledge manager 104 togenerate answers to cases. The network 140 may include local networkconnections and remote connections in various embodiments, such thatknowledge manager 104 may operate in environments of any size, includinglocal (e.g., a LAN) and global (e.g., the Internet). Additionally,knowledge manager 104 serves as a front-end system that can makeavailable a variety of knowledge extracted from or represented indocuments, network-accessible sources and/or structured data sources. Inthis manner, some processes populate the knowledge manager, with theknowledge manager also including input interfaces to receive knowledgerequests and respond accordingly.

In one embodiment, a content creator 130 creates content (e.g., adocument) in a knowledge base 106 for use as part of a corpus of dataused in conjunction with knowledge manager 104. In selected embodiments,the knowledge base 106 may include any file, text, article, or source ofdata (e.g., scholarly articles, dictionary definitions, encyclopediareferences, and the like) for use by the knowledge manager 104. Contentusers 130 may access the knowledge manager 104 via a network connectionor an Internet connection to the network 140, and may input questions tothe knowledge manager 104 that may be answered by the content in thecorpus of data.

As further described below, when a process evaluates a given section ofa document for semantic content, the process can use a variety ofconventions to query it from the knowledge manager 104. One conventionis to send a well-formed question. As used herein, semantic contentbroadly refers to content based upon the relation between signifiers,such as words, phrases, signs, and symbols, and what they stand for,their denotation, or connotation. In other words, semantic content iscontent that interprets an expression, such as by using Natural Language(NL) Processing. In one embodiment, the process sends well-formedquestions (e.g., Natural Language questions, etc.) to the knowledgemanager 104. In various embodiments, the knowledge manager 104 mayinterpret the question and provide a response to the content usercontaining one or more answers to the question. In some embodiments, theknowledge manager 104 may provide a response to users in a ranked listof answers.

In some illustrative embodiments, QA system 100 may be the IBM Watson™QA system available from International Business Machines Corporation ofArmonk, New York, which is augmented with the mechanisms of theillustrative embodiments described hereafter. The IBM Watson™ knowledgemanager system may receive an input question which it then parses toextract the major features of the question, that in turn are then usedto formulate queries that are applied to the corpus of data. Based onthe application of the queries to the corpus of data, a set ofhypotheses, or candidate answers to the input question, are generated bylooking across the corpus of data for portions of the corpus of datathat have some potential for containing a valuable response to the inputquestion.

The IBM Watson™ QA system then performs deep analysis on the language ofthe input prioritized question 119 and the language used in each of theportions of the corpus of data found during the application of thequeries using a variety of reasoning algorithms. There may be hundredsor even thousands of reasoning algorithms applied, each of whichperforms different analysis (e.g., comparisons), and generates a score.For example, certain reasoning algorithms may look at the matching ofterms and synonyms within the language of the input question and thefound portions of the corpus of data. Other reasoning algorithms maylook at temporal or spatial features in the language, while yet othersmay evaluate the source of the portion of the corpus of data andevaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e. candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. The QA system 100 thengenerates an output response or answer 120 with the final answer andassociated confidence and supporting evidence. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

Types of information processing systems that can utilize QA system 100range from small handheld devices, such as handheld computer/mobiletelephone 150 to large mainframe systems, such as mainframe computer158. Examples of handheld computer 150 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and Compact Disc players. Other examples ofinformation processing systems include pen, or tablet, computer 152,laptop, or notebook, computer 154, personal computer system 156, server162, and mainframe computer 158.

As shown, the various information processing systems can be networkedtogether using computer network 140. Types of computer network 140 thatcan be used to interconnect the various information processing systemsinclude PAN, LANs, Wireless Local Area Networks (WLANs), the Internet,the PSTN, other wireless networks, and any other network topology thatcan be used to interconnect the information processing systems.

In selected embodiments, the information processing systems includenonvolatile data stores, such as hard drives and/or nonvolatile memory.Some of the information processing systems may use separate nonvolatiledata stores. For example, server 162 utilizes nonvolatile data store164, and mainframe computer 158 utilizes nonvolatile data store 160. Thenonvolatile data store can be a component that is external to thevarious information processing systems or can be internal to one of theinformation processing systems. An illustrative example of aninformation processing system showing an exemplary processor and variouscomponents commonly accessed by the processor is shown in FIG. 2.

In various embodiments, the QA system 100 is implemented to receive avariety of data from various computing devices (e.g., 150, 152, 154,156, 158, 162) and data sources 166, which in turn is used to perform QAoperations described in greater detail herein. In certain embodiments,the QA system 100 may receive a first set of information from a firstcomputing device (e.g., laptop computer 154). The QA system 100 thenuses the first set of data to perform QA processing operations resultingin the generation of a second set of data, which in turn is provided toa second computing device (e.g., server 162). In response, the secondcomputing device may process the second set of data to generate a thirdset of data, which is then provided back to the QA system 100. In turn,the QA system may perform additional QA processing operations on thethird set of data to generate a fourth set of data, which is thenprovided to the first computing device.

In certain embodiments, a first computing device (e.g., server 162) mayreceive a first set of data from the QA system 100, which is thenprocessed and provided as a second set of data to another computingdevice (e.g., mainframe 158). The second set of data is processed by thesecond computing device to generate a third set of data, which isprovided back to the first computing device. The second computing devicethen processes the third set of data to generate a fourth set of data,which is then provided to the QA system 100, where it is used to performQA operations described in greater detail herein.

In one embodiment, the QA system may receive a first set of data from afirst computing device (e.g., handheld computer/mobile device 150),which is then used to perform QA operations resulting in a second set ofdata. The second set of data is then provided back to the firstcomputing device, where it is used to generate a third set of data. Inturn, the third set of data is provided back to the QA system 100, whichthen provides it to a second computing device (e.g., mainframe computer158), where it is used to perform post processing operations.

As an example, a content user 130 may ask the question, “I'm looking fora good pizza restaurant nearby.” In response, the QA system 100 mayprovide a list of three such restaurants in a half mile radius of thecontent user. In turn, the content user 130 may then select one of therecommended restaurants and ask for directions, signifying their intentto proceed to the selected restaurant. In this example, the list ofrecommended restaurants, and the restaurant the content user 130selected, would be the third set of data provided to the QA system 100.To continue the example, the QA system 100 may then provide the thirdset of data to the second computing device, where it would be processedto generate a database of the most popular restaurants, byclassification, location, and other criteria.

In various embodiments the exchange of data between various computingdevices (e.g., 150, 152, 154, 156, 158, 162) results in more efficientprocessing of data as each of the computing devices can be optimized forthe types of data it processes. Likewise, the most appropriate data fora particular purpose can be sourced from the most suitable computingdevice (e.g., 150, 152, 154, 156, 158, 162), or data source 166, therebyincreasing processing efficiency. Skilled practitioners of the art willrealize that many such embodiments are possible and that the foregoingis not intended to limit the spirit, scope or intent of the invention.

FIG. 2 illustrates an information processing system 202, moreparticularly, a processor and common components, which is a simplifiedexample of a computer system capable of performing the computingoperations described herein. Information processing system 202 includesa processor unit 204 that is coupled to a system bus 206. A videoadapter 208, which controls a display 210, is also coupled to system bus206. System bus 206 is coupled via a bus bridge 212 to an Input/Output(I/O) bus 214. An I/O interface 216 is coupled to I/O bus 214. The I/Ointerface 216 affords communication with various I/O devices, includinga keyboard 218, a mouse 220, a Compact Disc—Read-Only Memory (CD-ROM)drive 222, a floppy disk drive 224, and a flash drive memory 226. Theformat of the ports connected to I/O interface 216 may be any known tothose skilled in the art of computer architecture, including, but notlimited to Universal Serial Bus (USB) ports.

The information processing system 202 is able to communicate with aservice provider server 252 via a network 228 using a network interface230, which is coupled to system bus 206. Network 228 may be an externalnetwork such as the Internet, or an internal network such as an EthernetNetwork or a Virtual Private Network (VPN). Using network 228, theinformation processing system 202 is able to access a service providerserver 252 to implement the present invention.

A hard drive interface 232 is also coupled to system bus 206. Hard driveinterface 232 interfaces with a hard drive 234. In a preferredembodiment, hard drive 234 populates a system memory 236, which is alsocoupled to system bus 206. Data that populates system memory 236includes the information processing system's 202. operating system (OS)238 and software programs 244.

OS 238 includes a shell 240 for providing transparent user access toresources such as software programs 244. Generally, shell 240 is aprogram that provides an interpreter and an interface between the userand the operating system. More specifically, shell 240 executes commandsthat are entered into a command line user interface or from a file.Thus, shell 240 (as it is called in UNIX®), also called a commandprocessor in Windows®, is generally the highest level of the operatingsystem software hierarchy and serves as a command interpreter. The shellprovides a system prompt, interprets commands entered by keyboard,mouse, or other user input media, and sends the interpreted command(s)to the appropriate lower levels of the operating system (e.g., a kernel242) for processing. While shell 240 generally is a text-based,line-oriented user interface, the present invention can also supportother user interface modes, such as graphical, voice, gestural, etc.

As depicted, OS 238 also includes kernel 242, which includes lowerlevels of functionality for OS 238, including essential servicesrequired by other parts of OS 238 and software programs 244, includingmemory management, process and task management, disk management, andmouse and keyboard management. In certain embodiments, system memory 236may also include a Browser 246 having program modules and instructionsenabling a World Wide Web (WWW) client (i.e., information processingsystem 202) to send and receive network messages to the Internet usingHyperText Transfer Protocol (HTTP) messaging, thus enablingcommunication with service provider server 252. In various embodiments,system memory 236 may also include a header label assignment system 248.In various embodiments, header label assignment system 248 includes codefor implementing the processes described hereinbelow. In certainembodiments, the information processing system 202 is able to downloadthe header label assignment system 248 from a service provider server252.

The hardware elements depicted in the information processing system 202are not intended to be exhaustive, but rather are representative tohighlight components that may be used to implement the presentinvention. For instance, the information processing system 202 mayinclude alternate memory storage devices such as magnetic cassettes,DVDs, Bernoulli cartridges, and the like. These and other variations areintended to be within the spirit, scope and intent of the presentinvention.

The header label assignment system 248 is designed with an appreciationthat tables having data that can be used in an information processingsystem may include complex header structures. The design and operationof the header label assignment system 248 also appreciates that suchcomplex header structures may limit the ability of QA system 100 toanalyze information provided in such tables.

In appreciation of these limitations, certain embodiments of the headerlabel assignment system 248 execute operations that automaticallygenerate data structures that assign headers and header labels within acomplex table to corresponding data cells. In certain embodiments, thedata structures generated by the header label assignment system 248 maybe used in training a QA system so that the QA system is better preparedto analyze and provide answers to natural language questions.

FIG. 3 depicts one example of tabular information that may be used toprovide an understanding of certain embodiments of the disclosed system.In this example, the information is arranged as cells in atwo-dimensional data structure of rows and columns. In this example,table 305 shows the information in a text file format while table 310shows the information in a tabular format. The same data is expressed inboth tables 305 and 310. For purposes of the following discussion,however, table 310 uses with its implicit column numbers C1-C5 and rownumbers R1-R6 to describe positions of cells within the table 310. Itwill be recognized that the column numbers and row numbers shown intable 310 are provided for indexing purposes and are not necessarilyincluded as cells in either table 305 or 310.

In certain embodiments, each cell of a table is populated with at leastone of a header name, data value, or no information. With reference tothe example shown in table 310, “header names” are shown in bold type,“data cells” include dollar values, and “no information” is shown as anempty cell place holder. Certain cells also constitute headers for otherheader cells, which are shown in bold italics. Certain embodiments ofthe header label assignment system recognize that such headers for otherheader cells, referenced herein as “labels,” may be used to giveadditional context to data cells of the table.

Using indices for the relative position of cells within the table 310 asexamples, the header name “NYC BOROUGH” is located within the table atindexed locations R1C1 and R2C1. The header name “SPEEDING TICKETS” islocated within the table at indexed locations R1C2 and R1C3. The headername “PARKING TICKETS” is located within the table at indexed locationsR1C4 and R1C5. The header name “CAR” is located within the table atindexed location R2C2. The data cell “1500” is located within the tableat R4C2.

For a computer system to intelligently understand and search data withintables, it needs to correctly identify the headers which apply contextto each data cell. In certain embodiments, the data cell “500” may beassociated with headers BROOKLYN”, “SUV”, and “SPEEDING TICKETS” ascontext to the data cell. Certain embodiments of the disclosed systemimprove the context by additionally labeling the headers. For example,the “BROOKLYN” header can be labeled as a “NYC BOROUGH”. This providesadditional value in the data extraction for improved search and answerretrieval. For example, in order to answer the question “What NYCborough had 500 SUV speeding tickets?” from table 310, certainembodiments of the system associate data cell “500” with its row header“BROOKLYN”, and then associate “BROOKLYN” with label “NYC BOROUGH.”

FIG. 4 is a flowchart depicting exemplary operations that may beexecuted in certain embodiments of the header label assignment system.In certain embodiments, the operations shown in FIG. 4 associate tableheaders to data cells and can handle more complex tables includingnested headers such as those shown in table 310. In certain embodiments,the operations shown in FIG. 4 also generates an object for each headercell that includes a list of headers that may be reviewed to identifylabel candidates for the header cell. In certain embodiments, one ormore such candidates are assigned as a label that is to be associatedwith the name contained in the header cell. In certain embodiments, theassigned labels are included in data cell objects having a formatteddata structure that may be easily consumed by search engines, QAsystems, etc.

In certain embodiments, it is assumed that each table cell is alreadyempty or labeled as either a header or data. In certain embodiments, theheader label assignment system may make a single pass over thetwo-dimensional table array and associate each data cell with itsappropriate headers. In certain embodiments, values are obtained fromthe cells in each row and column of the table at operation 405. Incertain embodiments, a current list of column headers is maintained. Incertain embodiments, a current list of row headers is also maintained.

In certain embodiments, the header label assignment system may includeoperations to address the presence of embedded headers in a table. Tothis end, in certain embodiments, the header label assignment systemkeeps a count of consecutive column headers and, in certain embodiments,a count of consecutive row headers at operation 410. At operation 415,the list of column headers is updated based on the count of consecutivecolumn headers, and the list of row headers is based on the count ofconsecutive row headers. In certain embodiments, headers from thecurrent list of column headers and current list of row headers areassociated with the cell at operation 420. In certain embodiments, whenthe current list of row headers is associated with the data cell atoperation 420, the counts for the consecutive column headers andconsecutive row headers are reset. In certain embodiments, theoperations shown in FIG. 4 are continued until all data cells of thetable have been associated with one or more headers. The resulting datastructure in which each data cell is associated with at least one headermay be stored for subsequent ingestion and use by, for example, a QAsystem and/or search engine.

In certain embodiments, the context of one or more of the data cells maybe enhanced using label headers. In certain embodiments, label headersmay be identified using the header list associated with a header cell.In certain embodiments, label candidates are identified from the headerlist of each header cell at operation 425. Once the label candidateshave been identified, certain embodiments associate headers in a datacell with one or more labels from the identified label candidates atoperation 430.

FIG. 5 is another flowchart depicting exemplary operations that may beexecuted in certain embodiments of the header label assignment system.In certain embodiments, the header label assignment system initializesempty header arrays at operation 502. In certain embodiments, the headerlabel assignment system initializes an empty column header array foreach column of the table. In certain embodiments, the header labelassignment system also initializes an empty row header array that isused as each column of a row is traversed. In certain embodiments, theheader label assignment system initializes an empty label candidateheader array to be used to store headers associated with each headercell encountered in the table.

In certain embodiments, the initialization operations executed atoperation 502 include the initialization of certain counters. In certainembodiments, consecutive header counters are initialized including aconsecutive row header counter, CRHC for the indexed row being traversedby the table and a consecutive column header counter for each columnindex CCHC(i), where (i) corresponds to the column number of the cell inthe table.

In the example of FIG. 5, the initial row and column indices of the cellat which the header label assignment system is to begin processing thetable is set at operation 504. In certain embodiments, both the initialrow index and initial column index are set to 1 so that processingbegins at the first cell of the table indexed at R1C1. In certainembodiments, the header label assignment system may be configured toanalyze only a sub-table included in a data set, in which case the rowand column indices may be initialized to point to the first cell of thesub-table.

In certain embodiments, the initial row index and initial column indexare used to retrieve a cell at the indexed row and column positionswithin the table at operation 506. At operation 508, a determination ismade as to whether the cell at the indexed row and column position is aheader. If the cell at the indexed row and column position is a datacell, processing may proceed to the data cell operations shown in FIG. 6and described in detail herein. Otherwise, if a determination is madethat the cell at the indexed row and column position is a header, thevalue of the CRHC is incremented at operation 510 and the value of theCCHC(i), for the indexed column is incremented at operation 512.

In certain embodiments, the header label assignment system may executeoperations to update the column header array for the indexed columnand/or the row header array for the indexed row. Certain embodiments mayaddress the presence of embedded headers in a table by updating thecolumn header array and/or row header array based on one or more of theconsecutive header counts CRCH and CCHC(i). In certain embodiments, adetermination that a header from the row header array is to be removedis made at operation 514 based on the value of CRCH. In certainembodiments, the header label assignment system determines that a rowheader from the row header array is to be removed when the length of therow header array is greater than or equal to the value of CRCH. Incertain embodiments, if the determination at operation 514 indicatesthat a row header from the row header array is to be removed, it isremoved at operation 516.

In certain embodiments, which header is to be removed from the rowheader array for the indexed row is dependent on the value of CRCH. Incertain embodiments, when the value of CRCH=1, the row header that isremoved is the last row header in the current version of the row headerarray. In certain embodiments, when the value of CRCH=2, the row headerthat is removed is the second from last row header in the currentversion of the row header array. In certain embodiments, when the valueof CRCH=n, the row header that is removed is the n^(th) from last rowheader in the current version of the row header array.

In certain embodiments, the header label assignment system proceeds todetermine whether a header from the column header array for the indexedcolumn is to be removed at operation 518. In certain embodiments, adetermination that a header from the column header array where theindexed column is to be removed is based on the value of CCCH(i) for theindexed column. In certain embodiments, the header label assignmentsystem determines that a header from the column header array is to beremoved when the length of the column header array for the indexedcolumn is greater than or equal to the value of CCCH(i). In certainembodiments, if the determination at operation 518 indicates that acolumn header from the column header array is to be removed, it isremoved at operation 520.

In certain embodiments, which header is to be removed from the columnheader array for the indexed column is dependent on the value ofCCCH(i). In certain embodiments, when the value of CCCH(i)=1, the columnheader that is removed is the last column header in the current versionof the column header array. In certain embodiments, when the value ofCCCH(i)=2, the column header that is removed is the second from lastcolumn header in the current version of the column header array. Incertain embodiments, when the value of CCCH(i)=x, the column header thatis removed is the x^(th) from last column header in the current versionof the column header array.

In certain embodiments, the row header array, indexed column headerarray are updated with the value of the indexed cell once operations514-520 have been addressed. To this end, the value of the indexed cellmay be appended to the row header array at operation 522 and may beappended to the indexed column header array at operation 524.

At operation 525, the indexed cell is associated with the current columnheader array for the indexed column and the current row away for theindexed row. In certain embodiments, if the cell is a data cell, theheader label assignment system may generate and store a data cell objectincluding headers associated with the data found in the indexed datacell. In certain embodiments, if the cell is a header cell, the headerlabel assignment system may generate and store a header cell objectincluding headers associated with the header name found in the indexeddata cell.

In certain embodiments, the columns of each indexed row are sequentiallytraversed in the header assignment operations before proceeding to ananalysis of the columns of the next indexed row. To this end, adetermination is made at operation 526 whether the indexed row of thetable has additional columns. If so, the column index is incremented atoperation 528 and the newly incremented column index and current rowindex are used to retrieve the corresponding cell value at operation506. If all of the columns of the indexed row have been analyzed, theheader label assignment system may determine whether the table includesmore rows that are to be analyzed at operation 530. If more rows are tobe analyzed, the row index may be incremented at operation 532; thecontent of the row array may be reset at operation 534; and the columnindex may be reset to its initial value at and the CRCH reset atoperation 536. In certain embodiments, the row index incremented atoperation 532 and the value of the reset column index are used toretrieve the corresponding cell value at operation 506.

FIG. 6 is a flowchart depicting exemplary operations that may beexecuted when a determination is made that the cell at the indexed rowand indexed column retrieved at operation 506 (FIG. 5) is a data cell.In certain embodiments, the value of CRCH for the indexed row is resetat operation 606 and value of CCHC(i) for the indexed column is reset atoperation 608 before returning to the operations of FIG. 5.

FIG. 7 is a flowchart depicting exemplary operations that may be used toidentify label candidates using header cell objects generated, forexample, in the operations shown in FIG. 5. In certain embodiments, theheader label assignment system has generated header cell objects foreach of the header cells within the table 310. In certain embodiments,each header cell has a corresponding header cell object header(x), where(x) is an index identifying the header cell object. In certainembodiments, each header cell object (x) includes a list of header namesnumbering (y), where (y) is an index pointing to a header name in thecorresponding header cell object (x).

In certain embodiments, each header name in a header object may beinitially treated as a potential label candidate. In the operations ofFIG. 7, the header label assignment system identifies headers that mayactually be a label candidate from the list of header names of eachheader cell object and, in certain embodiments, associates the labelswith corresponding headers in data cells.

In certain embodiments, index pointers for (x) and (y) are generated atoperation 705. In certain embodiments, the header label assignmentsystem gets object header (x) at operation 705 and accesses thepotential label candidate(y) from object header(x) at operation 710. Incertain embodiments, a determination is made at operation 715 as towhether the potential candidate(y) is adjacent a data cell. If thepotential candidate(y) is adjacent a data cell, it is removed as apotential candidate from the header(x) object at operation 720. Incertain embodiments, a determination is made at operation 725 as towhether the potential candidate(y) has already been used as a headerlabel. If the potential candidate(y) has already been used as a labelheader, it is removed as a potential candidate from header(x) atoperation 730. In certain embodiments, a determination is made atoperation 735 as to whether there are more potential candidates in theheader(x) object. If there are more potential candidates remaining inthe header(x) object, the value for the index(y) pointer is incrementedat operation 740 and the corresponding candidate is retrieved atoperation 710. In certain embodiments, operations 715-735 are repeateduntil all potential candidates in the header(x) object have beenanalyzed.

In certain embodiments, once all of the label candidates have beenidentified for header(x), a label is assigned to the header cellcorresponding to the header(x) object at operation 745. In certainembodiments, the label assigned to the header cell corresponds to thelabel candidate that is closest to the header cell within the table 310.

In certain embodiments, the header label assignment system iterativelyexecutes the foregoing operations for each header object. To this end, adetermination may be made at operation 750 as to whether there are moreheader objects that are to be analyzed for assignment as a header label.If there are more header objects, the value for index(x) may beincremented at operation 755 and the next header object for analysis isaccessed at operation 705.

In certain embodiments, the labels are associated with correspondingheaders in the data cell objects at operation 760. In certainembodiments, the header labels for the headers may be stored as one ormore objects that are separate from the data cell objects. In suchembodiments, header labels are associated with the headers within thedata cell object during, for example, execution of a search. In certainembodiments, the data cell objects are themselves updated with labelsfor corresponding headers. In such embodiments, searches includingheader labels may be executed on the updated data cell objects.

FIG. 8 shows one example of a data structure 800 that may be generatedfrom the table information shown in FIG. 3 using certain embodiments ofthe header label assignment system. In the specific example shown inFIG. 8, the header label assignment system has provided a JSON output inwhich the individual data cells of the table are associated with one ormore respective headers and the headers are associated withcorresponding labels. In certain embodiments, header label “NYC BOROUGH”has been associated with header names “MANHATTAN” and “BROOKLYN in eachdata cell. In certain embodiments, the data structure 800 may be used bya QA system or search engine. When the QA system or search engine isasked to answer questions that reference the label “NYC BOROUGH” withoutreference to specific borough names.

FIG. 9 shows another example of a table 900 including information fromwhich header labels may be extracted to enhance the context of the datacells. In this table, the names “Violation”, “Vehicle”, and “NYCBorough” have been identified by the header label assignment system asheader labels. The name “Violation” has been assigned as a label for theheaders “Speeding Ticket” and “Parking Ticket.” The name “Vehicle” hasbeen assigned as a label for header names “Car” and “SUV.” The name “NYCBorough” has been assigned as a label header for the header names“Manhattan”, “Brooklyn”, “Queens”, and “Bronx.” In certain embodiments,such label assignments may result in the data cell structures shown inJSON table 905.

FIG. 10 depicts one example of pseudocode 1005 that may be used as abasis to implement certain embodiments of the header label assignmentsystem. In certain embodiments, the header label assignment systemexecutes identifies candidate labels from the header objects using, forexample, operations represented by instructions exemplified inpseudocode 1005. In certain embodiments, once the header labelassignment system has identified the label candidates, the appropriatelabel candidates are assigned to the corresponding header using, forexample, operations represented by instructions exemplified in inpseudocode 1015.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without departing from the spirit and scope of the inventionas defined by the appended claims.

What is claimed is:
 1. A computer-implementable method for associatingdata cells with headers and header labels from tables having one or moreheader structures, comprising: receiving a table having rows andcolumns, wherein the table includes a plurality of cells, wherein eachcell is populated with at least one of a header name, data value, or noinformation; determining whether a cell is a header cell or data cell;if the cell is a header cell, dynamically updating a current list ofcolumn headers; and dynamically updating a current list of row headers;dynamically updating a list of header names for the header cell usingthe current list of column headers and current list of row headers; uponencountering a data cell, assigning the current list of column and rowheaders to the data cell; and identifying label candidates for theheader cell from the list of header names; and assigning one or morelabel candidates as labels to the header cell.
 2. The method of claim 1,wherein identifying label candidates for the header cell from the listof header names comprises: removing a header name as a label candidatefrom the list of header names if the header name is already used as alabel.
 3. The method of claim 1, wherein identifying label candidatesfor the header cell from the list of header names comprises: assigning alabel to the header cell using an identified label candidate that isclosest to the header cell within the table.
 4. The method of claimfurther comprising: if the cell is a header cell, incrementing a countof consecutive column headers for the column containing the cell;comparing the incremented count of consecutive column headers for thecolumn containing the cell with a length of an array holding the currentlist of column headers for the column containing the cell; and if thelength of the array holding the current list of column headers for thecolumn containing the cell is greater than the count of consecutivecolumn headers for the column containing the cell, replacing a precedingcolumn header in the current list of column headers containing the cellwith a value of the header cell, wherein the header replaced in thecurrent list of column headers is the x^(th) from last header of thecurrent list of column headers, where x is equal to the value of thecount of consecutive column headers.
 5. The method of claim furthercomprising: if the cell is a header cell, incrementing a count ofconsecutive row headers for the row containing the cell; comparing theincremented count of consecutive row headers for the row containing thecell with a length of an array holding the current list of row headersfor the row containing the cell; and if the length of the array holdingthe current list of row headers for the row containing the cell isgreater than the count of consecutive row headers for the row containingthe cell, updating the list of row headers for the row containing thecell by replacing a preceding row header in the current list of columnheaders containing the cell with a name of the header cell, wherein theheader replaced in the current list of row headers is the n^(th) fromlast header of the current list of column headers, where n is equal tothe value of the count of consecutive row headers.
 6. The method ofclaim 1, further comprising: updating a data structure for each datacell with one or more header labels associated with headers assigned tothe data cell.
 7. The method of claim 1, further comprising: storing thedata structure in electronic memory; electronically receiving a naturallanguage query; extracting from the natural language query, using anatural language processing engine, two or more query words; andsearching the stored data structure for a data structure containing alabel that matches the two or more query words, and returning the celldata stored in the data structure as a question answer.
 8. A systemcomprising: a processor; a data bus coupled to the processor; and anon-transitory, computer-readable storage medium embodying computerprogram code, the non-transitory, computer-readable storage medium beingcoupled to the data bus, the computer program code interacting with aplurality of computer operations and comprising instructions executableby the processor and configured for: receiving a table having rows andcolumns, wherein the table includes a plurality of cells, wherein eachcell is populated with at least one of a header name, data value, or noinformation; determining whether a cell is a header cell or data cell;if the cell is a header cell, dynamically updating a current list ofcolumn headers; and dynamically updating a current list of row headers;dynamically updating a list of header names for the header cell usingthe current list of column headers and current list of row headers; uponencountering a data cell, assigning the current list of column and rowheaders to the data cell; and identifying label candidates for theheader cell from the list of header names; and assigning one or morelabel candidates as labels to the header cell.
 9. The system of claim 8,wherein identifying label candidates for the header cell from the listof header names comprises: removing a header name as a label candidatefrom the list of header names if the header name is already used as alabel.
 10. The system of claim 8, wherein identifying label candidatesfor the header cell from the list of header names comprises: assigning alabel to the header cell using an identified label candidate that isclosest to the header cell within the table.
 11. The system of claim 8,wherein the instructions are further configured for: if the cell is aheader cell, incrementing a count of consecutive column headers for thecolumn containing the cell; comparing the incremented count ofconsecutive column headers for the column containing the cell with alength of an array holding the current list of column headers for thecolumn containing the cell; and if the length of the array holding thecurrent list of column headers for the column containing the cell isgreater than the count of consecutive column headers for the columncontaining the cell, replacing a preceding column header in the currentlist of column headers containing the cell with a value of the headercell, wherein the header replaced in the current list of column headersis the x^(th) from last header of the current list of column headers,where x is equal to the value of the count of consecutive columnheaders.
 12. The system of claim 8, wherein the instructions are furtherconfigured for: if the cell is a header cell, incrementing a count ofconsecutive row headers for the row containing the cell; comparing theincremented count of consecutive row headers for the row containing thecell with a length of an array holding the current list of row headersfor the row containing the cell; and if the length of the array holdingthe current list of row headers for the row containing the cell isgreater than the count of consecutive row headers for the row containingthe cell, updating the list of row headers for the row containing thecell by replacing a preceding row header in the current list of columnheaders containing the cell with a name of the header cell, wherein theheader replaced in the current list of row headers is the n^(th) fromlast header of the current list of column headers, where n is equal tothe value of the count of consecutive row headers.
 13. The system ofclaim 8, wherein the instructions are further configured for: updating adata structure for each data cell with one or more header labelsassociated with headers assigned to the data cell.
 14. The system ofclaim 8, wherein the instructions are further configured for: storingthe data structure in electronic memory; electronically receiving anatural language query; extracting from the natural language query,using a natural language processing engine, two or more query words; andsearching the stored data structure for a data structure containing alabel that matches the two or more query words, and returning the celldata stored in the data structure as a question answer.
 15. Anon-transitory, computer-readable storage medium embodying computerprogram code, the computer program code comprising computer executableinstructions configured for: receiving a table having rows and columns,wherein the table includes a plurality of cells, wherein each cell ispopulated with at least one of a header name, data value, or noinformation; determining whether a cell is a header cell or data cell;if the cell is a header cell, dynamically updating a current list ofcolumn headers; dynamically updating a current list of row headers;dynamically updating a list of header names for the header cell usingthe current list of column headers and current list of row headers; uponencountering a data cell, assigning the current list of column and rowheaders to the data cell; and identifying label candidates for theheader cell from the list of header names; and assigning one or morelabel candidates as labels to the header cell.
 16. The non-transitory,computer-readable storage medium of claim 15, wherein identifying labelcandidates for the header cell from the list of header names comprises:removing a header name as a label candidate from the list of headernames if the header name is already used as a label; and assigning alabel to the header cell using an identified label candidate that isclosest to the header cell within the table.
 17. The non-transitory,computer-readable storage medium of claim 15, wherein the instructionsare further configured for: if the cell is a header cell, incrementing acount of consecutive column headers for the column containing the cell;comparing the incremented count of consecutive column headers for thecolumn containing the cell with a length of an array holding the currentlist of column headers for the column containing the cell; and if thelength of the array holding the current list of column headers for thecolumn containing the cell is greater than the count of consecutivecolumn headers for the column containing the cell, replacing a precedingcolumn header in the current list of column headers containing the cellwith a value of the header cell, wherein the header replaced in thecurrent list of column headers is the x^(th) from last header of thecurrent list of column headers, where x is equal to the value of thecount of consecutive column headers.
 18. The non-transitory,computer-readable storage medium of claim 15, wherein the instructionsare further configured for: if the cell is a header cell, incrementing acount of consecutive row headers for the row containing the cell;comparing the incremented count of consecutive row headers for the rowcontaining the cell with a length of an array holding the current listof row headers for the row containing the cell; and if the length of thearray holding the current list of row headers for the row containing thecell is greater than the count of consecutive row headers for the rowcontaining the cell, updating the list of row headers for the rowcontaining the cell by replacing a preceding row header in the currentlist of column headers containing the cell with a name of the headercell, wherein the header replaced in the current list of row headers isthe n^(th) from last header of the current list of column headers, wheren is equal to the value of the count of consecutive row headers.
 19. Thenon-transitory, computer-readable storage medium of claim 15, whereinthe instructions are further configured for: updating a data structurefor each data cell with one or more header labels associated withheaders assigned to the data cell.
 20. The non-transitory,computer-readable storage medium of claim 15, wherein the instructionsare further configured for: storing the data structure in electronicmemory; electronically receiving a natural language query; extractingfrom the natural language query, using a natural language processingengine, two or more query words; and searching the stored data structurefor a data structure containing a label that matches the two or morequery words, and returning the cell data stored in the data structure asa question answer. receiving a table having rows and columns, whereinthe table includes a plurality of cells, wherein each cell is populatedwith one of a header name, data value, or no information; determiningwhether a cell is a header cell or data cell; if the cell is a headercell, maintaining a count of consecutive column headers; dynamicallyupdating a current list of column headers based on the count of theconsecutive column headers; and upon encountering a data cell, assigningthe current list of column headers to the data cell.